That last “advantage” may turn out to be a problem. Some amount of piracy is to the advantage of software companies. If some user really would not have bought your software at any price, you haven’t lost anything if he uses a pirated copy. In fact you gain, because he is one more user helping to make your software the standard– or who might buy a copy later, when he graduates from high school.

The application that pushed desktop computers out into the mainstream was VisiCalc, the first spreadsheet. It was written by two guys working in an attic, and yet did things no mainframe software could do. VisiCalc was such an advance, in its time, that people bought Apple IIs just to run it. And this was the beginning of a trend: desktop computers won because startups wrote software for them.

E. B. White was amused to learn from a farmer friend that many electrified fences don’t have any current running through them. The cows apparently learn to stay away from them, and after that you don’t need the current. “Rise up, cows!” he wrote, “Take your liberty while despots snore!”

One way to put up barriers to entry is through patents. But patents may not provide much protection. Competitors commonly find ways to work around a patent. And if they can’t, they may simply violate it and invite you to sue them. A big company is not afraid to be sued; it’s an everyday thing for them. They’ll make sure that suing them is expensive and takes a long time. Ever heard of Philo Farnsworth? He invented television. The reason you’ve never heard of him is that his company was not the one to make money from it. The company that did was RCA, and Farnsworth’s reward for his efforts was a decade of patent litigation.

It may seem unlikely in principle that one individual could really generate so much more wealth than another. The key to this mystery is to revisit that question, are they really worth 100 of us? Would a basketball team trade one of their players for 100 random people? What would Apple’s next product look like if you replaced Steve Jobs with a committee of 100 random people? These things don’t scale linearly. Perhaps the CEO or the professional athlete has only ten times (whatever that means) the skill and determination of an ordinary person. But it makes all the difference that it’s concentrated in one individual.

Or consider watches. Fifty years ago, by spending a lot of money on a watch you could get better performance. When watches had mechanical movements, expensive watches kept better time. Not any more. Since the invention of the quartz movement, an ordinary Timex is more accurate than a Patek Philippe costing hundreds of thousands of dollars. Indeed, as with expensive cars, if you’re determined to spend a lot of money on a watch, you have to put up with some inconvenience to do it: as well as keeping worse time, mechanical watches have to be wound.

It’s hard to predict what life will be like in a hundred years. There are only a few things we can say with certainty. We know that everyone will drive flying cars, that zoning laws will be relaxed to allow buildings hundreds of stories tall, that it will be dark most of the time, and that women will all be trained in the martial arts. Here I want to zoom in on one detail of this picture. What kind of programming language will they use to write the software controlling those flying cars?

Eric Raymond has written an essay called “How to Become a Hacker,” and in it, among other things, he tells would-be hackers what languages they should learn. He suggests starting with Python and Java, because they are easy to learn. The serious hacker will also want to learn C, in order to hack Unix, and Perl for system administration and cgi scripts. Finally, the truly serious hacker should consider learning Lisp:

Lisp is worth learning for the profound enlightenment experience you will have when you finally get it; that experience will make you a better programmer for the rest of your days, even if you never actually use Lisp itself a lot.

Eric Raymond has written an essay called “How to Become a Hacker,” and in it, among other things, he tells would-be hackers what languages they should learn. He suggests starting with Python and Java, because they are easy to learn. The serious hacker will also want to learn C, in order to hack Unix, and Perl for system administration and cgi scripts. Finally, the truly serious hacker should consider learning Lisp:Lisp is worth learning for the profound enlightenment experience you will have when you finally get it; that experience will make you a better programmer for the rest of your days, even if you never actually use Lisp itself a lot.

So here we have two pieces of information that I think are very valuable. In fact, I know it from my own experience. Number 1, languages vary in power. Number 2, most managers deliberately ignore this. Between them, these two facts are literally a recipe for making money. ITA is an example of this recipe in action. If you want to win in a software business, just take on the hardest problem you can find, use the most powerful language you can get, and wait for your competitors’ pointy-haired bosses to revert to the mean.

Let yourself be second guessed. When you make any tool, people use it in ways you didn’t intend, and this is especially true of a highly articulated tool like a programming language. Many a hacker will want to tweak your semantic model in a way that you never imagined. I say, let them; give the programmer access to as much internal stuff as you can without endangering runtime systems like the garbage collector.

A friend of mine rarely does anything the first time someone asks him. He knows that people sometimes ask for things that they turn out not to want. To avoid wasting his time, he waits till the third or fourth time he’s asked to do something; by then, whoever’s asking him may be fairly annoyed, but at least they probably really do want whatever they’re asking for.

People who do good work often think that whatever they’re working on is no good. Others see what they’ve done and are full of wonder, but the creator is full of worry. This pattern is no coincidence: it is the worry that made the work good.

The difference between design and research seems to be a question of new versus good. Design doesn’t have to be new, but it has to be good. Research doesn’t have to be good, but it has to be new. I think these two paths converge at the top: the best design surpasses its predecessors by using new ideas, and the best research solves problems that are not only new, but actually worth solving. So ultimately we’re aiming for the same destination, just approaching it from different directions.